The growth and advancement in World Wide Web and e-commerce has triggered the necessity for efficient online personalized recommendation system. The online search websites (e.g. Google, Bing) and e-commerce websites (e.g. Amazon, eBay) recommends the items to the user with other appropriate search criteria or associated items respectively. Recommendation engines use similarities of users and similarities between items to extract the information from large volume of historical transaction data to recommend the items to the user in a more appropriate way. These recommendation helps the retailers to cross-sell/up-sell certain product or service to a customer. Though there are several recommendation engines available which are good at mining information about frequently occurring items however there is very less work done on mining information about infrequent items. Also, the existing methods may be inefficient and may result in low accuracy of prediction and recommendations with respect to in-frequent items. Further, there are no works reported on efficient recommendation by combining frequent and in-frequent items in an ecommerce environment.
In view of forgoing discussion, there is a need for developing efficient methods and systems which mine information about both frequent and infrequent items from the historical transaction data, and personalize recommendation based on users' short-term behavior and long-term preferences.